SELF OPTIMIZING RESIDENTIAL AND COMMUNITY WiFi NETWORKS

ABSTRACT

A method for improving performance in a residential/community WiFi network is implemented on a self-optimizing network (SON) server and includes: receiving current configuration details and local performance statistics from SON clients installed in access points (APs) in the residential/community WiFi networks, where at least one of the APs is a residential AP configured to provide WiFi connectivity to both authorized users of the residential AP and a community of WiFi users not associated with the residential AP, analyzing at least the current configuration details and local performance statistics to identify performance issues in the residential/community WiFi network, determining remedial actions based on the analyzing, and instructing the access points to perform the remedial actions via the SON clients.

RELATED APPLICATION INFORMATION

The present application claims the benefit of priority from U.S.Provisional Patent Application, Ser. No. 62/232,034, filed on Sep. 24,2015.

FIELD OF THE INVENTION

The present invention generally relates to the optimization ofresidential and/or community WiFi based networks.

BACKGROUND OF THE INVENTION

Multiple service operators (MS Os) have recently been investingresources in the rollout of both massive densification of WiFi servicein the residential space, as well as community WiFi (hotspots) in anationwide deployment, with the end goal being to leverage nominallyunassociated home and/or commercial devices to provide a ubiquitous,cellular like coverage layer which will be able to provide servicecontinuity for a wide variety of services throughout the servicecoverage area.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood and appreciated more fully fromthe following detailed description, taken in conjunction with thedrawings in which:

FIG. 1 is a partly pictorial illustration of an exemplaryresidential/community WiFi self-optimizing network, constructed andoperative in accordance with embodiments described herein;

FIG. 2 is a schematic illustration of the self-optimizing server of FIG.1;

FIGS. 3A and 3B are illustrations of transmission bands used by theaccess points of the system of FIG. 1; and

FIGS. 4-6 are flowcharts of processes performed by the self-optimizingserver of FIG. 2.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

A method for improving performance in a residential/community WiFinetwork is implemented on a self-optimizing network (SON) server andincludes: receiving current configuration details and local performancestatistics from SON clients installed in access points (APs) in theresidential/community WiFi networks, where at least one of the APs is aresidential AP configured to provide WiFi connectivity to bothauthorized users of the residential AP and a community of WiFi users notassociated with the residential AP, analyzing at least the currentconfiguration details and local performance statistics to identifyperformance issues in the residential/community WiFi network,determining remedial actions based on the analyzing, and instructing theaccess points to perform the remedial actions via the SON clients.

A residential/community WiFi network SON server includes: an I/O moduleoperative to receive current configuration details and local performancestatistics from SON clients installed in access points in theresidential/community WiFi network, where at least one of the APs is aresidential AP configured to provide WiFi connectivity to bothauthorized users of the residential AP and a community of WiFi users notassociated with the residential AP, a processor, and a centralized SONapplication operative to be executed by the processor, where thecentralized SON application includes: a centralized dynamic channelassignment function operative to determine dynamic channel assignmentsfor the APs based on collected measurements received via the I/O module,a centralized power control (cPC) function operative to reduce orincrease total transmit power for the APs based on a coverage threshold,and a multi-band steering (MBS) function operative to steer the APs to afrequency band based on their capabilities.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Controller based WiFi management solutions for enterprise deploymentsare known. However, using such solutions for residential and/orcommunity deployments is problematic, particularly due to scalability.The large variance in customer-premises equipment (CPE) vendors, modelsand level of management support is also a significant factor.Residential/community WiFi networks seek to leverage a disparatecollection of equipment that is typically not under the physical controlof the MSO, rendering a centralized uniform management approach quitedifficult.

Accordingly, residential/community WiFi networks typically suffer from aseries of issues that are of lesser concern for conventional enterprisesolutions. The service provider (SP) may struggle to balance the needsand rights of its users, both in the open as well as home domains,thereby impacting on the quality of experience (QoE) which the users mayexpect and receive. When deploying community WiFi service based on homeresidential gateways (RGs), the service may de facto compete for thesame radio resources (air time) as the home networks, thereby renderinghot spot service inefficient. Deployment may also be constrained bynon-optimal user association to access points (APs) from the perspectiveof total cluster performance (capacity etc.).

It will be appreciated by those of ordinary skill in the art thatself-optimizing networks (SONs) are known in the mobile space and arepredicated on the mobile operator's control of mobile cell towers and/orconnection protocols with mobile handsets to facilitate a variety ofoptimization algorithms to improve ongoing performance of a mobilenetwork. In accordance with embodiments described herein, suchalgorithms may be adapted for use by Internet Service Providers (ISPs)to optimize WiFi based self-optimizing networks that leverage naturallyexisting coverage provided by the excess bandwidth available to home andcommunity access points (APs).

A significant hindrance to the adaptation of mobile SON algorithms foruse in residential/commercial WiFi environments is that while mobilecell towers are typically in complete control of the mobile operator,both in terms of accessibility and configuration transparency,residential/community APs are largely (although not totally) under thecontrol of the users. This is less of an issue where the AP orresidential gateway (RG) is leased directly from the ISP. However, manysuch APs are owned and under the control of the end users. Under suchcircumstances, the ISPs not only do not have direct control of thedevices, they may not be familiar with their configuration and/or evenbe aware of the model in use.

The system architecture of embodiments described herein may therefore beflexible to accommodate the different levels ofcontrol/information/compatibility that may be encountered vis-a-vis thedeployed devices.

Reference is now made to FIG. 1 which illustrates an exemplaryresidential/community WiFi self-optimizing network 10. Network 10comprises centralized SON server 100, RGs 30 and RG 40. RGs 30 and 40represent member APs in network 10. RGs 30 comprise SON client 35 andradio resource management function (RRM) 50. RRM 50 represents acomponent, implemented in software, hardware or firmware that istypically implemented in residential gateways for radio resourcemanagement. SON client 35 is a component, implemented in software,hardware or firmware, for communication with server 100. RG 40 comprisesRRM 50, but does not comprise SON client 35. It will be appreciated thatRG 40 therefore represents a residential gateway that has not beenparticularly configured for use in communication with server 100.

One or more centralized SON servers 100 may be installed in the ISP'sdata center. Optionally there may be connectivity to an automatedconfiguration services management system (ACS) 20, such as, for example(but not necessarily), Cisco Prime® Home. It will be appreciated by aperson of ordinary skill in the art that the embodiments describedherein may be implemented to interact with any commercially availableACS system. Using Cisco Prime Home as an example, ACS 20 may beeffectively configured to communicate with a relevant population of RGs30 via SON client 35, such as depicted in FIG. 1. It will be appreciatedthat SON client 35 may be vendor agnostic; i.e., RGs 30 as depicted inFIG. 1 may be provided by different vendors and may have differentcapabilities. However, SON clients 35 may provide a generally commoninterface for interaction with server 100.

SON server 100 may be configured to receive current configurationdetails and local performance statistics from SON clients 35, and useSON clients 35 to provide corrective action to the participating RGs 30.In accordance with embodiments described herein, SON server 100 may alsobe configured to infer performance information and/or configurationdetails regarding devices without an installed client (such as RG 40)based on neighboring devices with clients (i.e., RGs 30) and/orinformation from the provider's data network. Accordingly, non-clientinstalled devices such as RG 40 may be at least in part optimized viasettings in the data network and/or as “collateral improvements” relatedto changes in configuration of RGs 30.

Accordingly, SON server 100 may be configured to provide remedies forthe salient issues noted above as follows:

Poor QoE that may typically be caused at least in part by highinterference in dense areas and/or causes for lack of success inmatching carrier grade voice standards, may be improved usingcloud-based transaction power management and channel selection. As QoEimproves, the evolution towards a full voice over WiFi implementationmay continue.

SON server 100 may therefore identify locations where QoE is notsatisfactory, according to SP policies (pre-defined in the SON platform)and will attempt to automatically fix those areas by various mechanismssuch as frequency retunes, power control of Aps, etc. It is noted thatthere may be “rogue” APs without clients that cannot be directlymanipulated by the SON (e.g., RG 40); these rouge APs may be mapped asconstraints to the SON algorithms. It is expected, however, that therewill be significant clusters (particularly in MDU—multi dwellingunit—environments) that will have a substantial percent of APs from thesame SP, thereby facilitating effective central control.

Server 100 may improve inefficient hotspot service by optimizing homenetwork parameters and load-balancing between hotspots to reduceexcessive default safety margins with minimal damage to the base homeservice. Managing this inherent tradeoff may be done conservatively,implementing safety margins that will limit the service level that canbe provided by hotspot service (e.g. only up to 2-3 users per AP orsimilar restrictions). However, SON server 100 may be configured tomanage these limitations dynamically; accordingly, over time thelimitations may be fine-tuned to become sensitive to the real impactthis service has on the home users. Additionally, a server 100 may beable configured to deploy load balancing algorithms between APs thatwill optimize the spatial available radio resources based on dynamictraffic distribution.

With regard to deployment constraints, server 100 will be configured toevaluate performance and optimization on a cluster level (a clusterbeing comprised of multiple geographically related APs), therebyproviding a more complete solution and enabling de facto optimization ofthe rogue devices without clients. Centralized radio resource managementthusly implemented in server 100 may provide “sticky bad apple”management, optimize device settings, and provide multi-band managementas measures to address sub-optimal user association and various coverageand capacity issues.

In accordance with embodiments described herein, server 100 may beconfigured to provide centralized dynamic channel assignment (cDCA).cDCA may include dynamic channel assignments to APs based on collectedmeasurements from APs while taking into account the inter relationsbetween the APs in order to accomplish interference management forbetter spectrum management and improving spectral efficiency, and systemcapacity.

In accordance with embodiments described herein, server 100 may also beconfigured to provide Coverage and Capacity Optimization (CCO)functionality to the devices in network 10. This functionality may becomprised of several sub-functions that work in conjunction with eachother, in order to accomplish better cluster performance and userexperience within the cluster. For example: a centralized power control(cPC) function may be operative to reduce or increase AP total transmitpower in order to solve coverage problems on one hand, but to avoid,and/or reduce, the interference induced by the AP in other cases. Anoptimal user Association Control (cOA) function may be operative toprovide a best user association scheme so that overall clusterperformance is optimized for all the users, not just for a specificuser. A multi band steering (MBS) function that based on an AP withdual-band, dual-concurrent (DBDC) capabilities, may map users accordingto their capabilities and steer them to most effectives bands based ontheir capabilities, radio conditions, and traffic consumption, as wellas based on the target band loading and quality instantaneous levels.

In accordance with embodiments described herein, server 100 may also beconfigured to provide hybrid hotspot (community WiFi) air timemanagement (cATM) thereby balancing between home and hotspot usage,preventing situations in which a community WiFi user, presumably locatedin poor RF conditions, can use up large amount of the AP resources andthus degrade the service to home users receiving service from this AP.

In accordance with embodiments described herein, server 100 may also beconfigured to provide dynamic load balancing (cDLB) including IEEE802.11ac settings by using centralized SON information about the radiotopology relations between APs deployed in a certain area, to offloadmomentarily loaded APs to relevant neighbor APs.

In accordance with embodiments described herein, server 100 may also beconfigured to provide mobility optimization (cMO). cMO is comprised ofseveral sub-functions that work in conjunction with each other, in orderto accomplish improved cluster performance and user experience withinthe cluster. For example: a fast mobility mitigation (FHM—FrequentHandover Mitigation) function may be operative to prevent a fast movinguser from camping on a managed AP, thereby preventing compromised systemperformance caused by excessive signaling and handovers. A function forsession continuity optimization including key distribution support maybe operative to control the delay associated with keys distribution byproviding a list of relevant handover targets by server 100 to relevantsystem elements. A VoWiFi (voice over WiFi) optimization function mayprovide seamless mobility and interference control. An inter-workingfunction to coordinate optimization with a mobile network may also beprovided.

Reference is now also made to FIG. 1 which is a block diagram of acomputing centralized SON server 100 constructed and operative inaccordance with embodiments described herein to provide an exemplarycentralized SON application 130 configured to provide SON services tonetwork 10 as described herein. Server 100 may be implemented as anysuitable computing device such as, but not limited to, a personalcomputer, laptop computer, computer tablet, or smartphone that may beoperative to provide the functionality described herein. It will beappreciated that in operation, the functionality of server 100 may beimplemented on multiple such computing devices; server 100 may bedepicted herein as a single device for the purposes of convenienceand/or clarity.

It will be appreciated by one of skill in the art that server 100comprises hardware and software components that may provide at least thefunctionality of the embodiments described herein. For example, server100 may comprise at least processor 110, I/O module 120, and centralizedSON application 130. I/O module 120 may be implemented as a transceiveror similar means suitable for transmitting and receiving data betweencomputing server 100 and another device. Such data received may be, forexample, represent performance data received either directly orindirectly from RGs 30 and 40. Data transmitted from server 100 may be,for example, instructions sent to RGs 30 and/or an administrativefunction for network 10.

Centralized SON application 130 may be any suitable applicationimplemented in software and/or hardware that may be operative tofacilitate the optimization of APs in network 10 as described herein.

It will be appreciated that server 100 may comprise more than oneprocessor 110. For example, one such processor 110 may be a specialpurpose processor operative to execute centralized SON application 130.Centralized SON application 130 comprises centralized dynamic channelassignment function (cDCA) 132, centralized power control function (cPC)135, and multi-band steering function (MBS) 138, each of which may beimplemented in software and/or hardware and may be employed as necessaryby centralized SON application 130 to monitor, analyze, and/or optimizevarious aspects of WiFi performance in RGs 30 and 40.

Reference is now made to FIG. 3A which depicts current channelallocations in the 2.4 Ghz band. As depicted in FIG. 3A, the 2.4 Ghzband comprises overlapping channels of 22 Mhz width that are spacedevery 5 Mhz in the band. Accordingly, in operation, it may be possibleto use only three channels without interference in a common location,typically channels 1, 6, and 11 as indicated by the emphasized channelsin FIG. 3A. It will be appreciated by one of ordinary skill in the art,that for at least this reason, APs are commonly pre-set to use one ofthese three channels.

Reference is now made to FIG. 3B which depicts current channelallocations in the 5 Ghz band. As depicted in FIG. 3B, the 5 Ghz bandcomprises non-overlapping channels, thereby enabling simultaneous use ofeach channel in the 5 Ghz band without interference in a commonlocation. It will be appreciated that only some APs may be configured touse the 5 Ghz band; but it will also be appreciated that the 5 Ghz maybe used to alleviate congestion/interference in the 2.4 Ghz band.

In accordance with embodiments described herein, cDCA 132 may beemployed by server 100 to address interference between access pointsusing overlapping frequencies in the 2.4 Ghz band by using performancedata from RGs 30 and 40 to dynamically reassign channels (typically setat initial configuration) to achieve higher throughput and coverage.Accordingly, cDCA may include functionality for receiving and processingindividual AP radio scans; scanning reports fed back by SON client 35 toserver 100; building an inter-AP interference matrix based on reportedBSSIDs (basic service set identifiers) where each AP can map RSSI(received signal strength indicator) from surrounding APs; calculatingan overlap between neighboring APs to facilitate intelligent discoveryof topology/geography based on the received data; and employing adynamic channel selection algorithm to exploit all available WiFichannels in both the 2.4 Ghz and 5 Ghz bands.

Reference is now made to FIG. 4 which is a flowchart of a cDCA process400 to be performed by cDCA 132 in accordance with embodiments describedherein. cDCA 132 may receive (step 410) performance data either directlyor indirectly from RGs 30 and 40. The received performance data mayfacilitate topology discovery vis-a-vis RGs 30 and 40 within network 10.Based on a perceived topology, cDCA may form clusters of APs (e.g., RGs30 and 40) according to radio frequency (RF data received in step 410.

cDCA 132 may build (step 430) an inter-cell dependency matrix (ICDM) foreach of the APs in a given cluster. cDCA 132 may measure (step 440) theperformance for each of the APs. For example, measuring interference perchannel, measuring load per channel (saturation), measuring AP capacity,measuring user RSSI/signal-to-noise ratios (SNRs) and throughputsbetween APs in the ICDM.

Based on the performance measurements of step 440, cDCA may identify(step 450) one or more underperforming APs in the cluster. cDCA mayminimize (step 460) a cost function for changing the frequency used forthe worst performing AP of the APs identified in step 450, and based onthe results of the function, instruct the AP to change the frequency,i.e., channel, used by the AP.

cDCA may receive (step 480) feedback regarding the efficacy of theinstruction to change frequency in the form of performance datagenerally similar to that received in step 410. Processing control maythen return to step 440 to reassess the performance of the APs in the RFcluster. It will be appreciated that process 400 may be implemented withan anti-oscillation mechanism based on the feedback received in step 480to prevent never-ending repeating correction loops. For example, cDCAmay track such repeating corrections and impose a processing delay whenthe same correction is input and reversed X number of times.Alternatively, or in addition, cDCA may be configured to skipoptimization of one or more such oscillating APs in favor of otherunder-performing APs that have not yet been processed.

In accordance with embodiments described herein, cPC 135 may addressinterference between Access Points caused by excessive power based onuser measurements (typically set at maximum by default) to achievehigher throughput and coverage. Accordingly, cPC may implement coveragevs interference tradeoff management, based on AP reports includingassociated users (home, public) to all SSIDs (service set identifiers)and their RF conditions, for both download (DL) and upload (UL). It willbe appreciated that in current deployments, most APs are set by defaultto max power, leading to unnecessary interferences. cPC 135 maytherefore map the coverage level of each SSID and match it to desiredthresholds (e.g. for managed video SSID the desired coverage thresholdmay be >75 dBm), and reduce/increase power where applicable.Accordingly, this feature may optimize the power transmitted by APs tomaintain sufficient power levels in home coverage while minimizing theexternal interference caused by the AP.

Reference is now made to FIG. 5 which is a flowchart of a cPC process500 to be performed by cPC 135 in accordance with embodiments describedherein. cPC 135 may receive (step 510) performance data either directlyor indirectly from RGs 30 and 40. It will be appreciated that step 510may be performed as part of a generic data collection step together withstep 410.

cPC 135 may use the received performance data to map (step 520) coveragelevels per SSID. Based on the map from step 420, cPC 135 may detect(step 530) potential over/under-powered conditions in one or moreassociated APs, and instruct (step 540) the AP(s) to reduce/increasepower accordingly.

cPC may receive (step 550) feedback regarding the efficacy of theinstruction to reduce/increase power in the form of performance datagenerally similar to that received in step 510. Processing control maythen return to step 520 to remap the coverage level of the affectedAP(s). It will be appreciated that process 500 may be implemented withan anti-oscillation mechanism based on the feedback received in step 550to prevent never-ending repeating correction loops. For example, cPC maytrack such repeating corrections and impose a processing delay when thesame power adjustment is input and reversed X number of times.Alternatively, or in addition, cPC may be configured to compensate fornon-optimal power consumption by a given AP by adjusting the powerconsumption of a one or more neighboring APs.

In accordance with embodiments described herein, multi-band steering(MBS) addresses load balancing between 2.4 Ghz and 5 Ghz bands by movingstatic devices to the 5 Ghz band without losing coverage to provide acleaner 2.4 Ghz layer for voice applications. This entails forcing userdevices to be associated with different SSIDs possibly in contradictionto their default association decision (RSSI based). This associationpolicy may be managed based on service type and/or user type(mobile/stationary or other). Accordingly, multi-band steering mayprovide: a cleaner 2.4G band that may be leveraged as a contiguouscoverage band for VoWiFi service, and reduced hotspot/home contention onthe coverage band.

Reference is now made to FIG. 6 which is a flowchart of an MBS process600 to be performed by MBS 138 in accordance with embodiments describedherein. MBS 138 may receive (step 610) performance data either directlyor indirectly from RGs 30 and 40. It will be appreciated that step 610may be performed as part of a generic data collection step together withstep 410 and/or 510.

MBS 138 may use the received performance data to identify (step 620)devices that may preferably use the 5 Ghz band instead of the 2.4 Ghz.Such identification may be predicated at least on a capability of theassociated device to operate on the 5 Ghz band (as noted hereinabove,not all APs are configured with the functionality for using the 5 Ghzband). Other factors that may be considered include, for example, thetype of services used by the device and/or a user type, i.e., mobile orstationary.

After an AP has been identified in step 620, MBS 138 may employ avariety of steps to steer the AP to the 5 Ghz band. For example, MBS 138may use passive steering. Examples of possible passive steering stepsmay include MBS 138 adding (step 630) the AP to a non-response list for2.4 Ghz probes. It will be appreciated that an AP may initiate a probefor a given channel/band before selecting the channel/band for use. Inthe absence of a response to such a probe, an AP may be passivelydissuaded from using a given channel/band. Alternatively, MBS 138 mayadd (step 640) the AP to a delayed response list for 2.4 Ghz probes; MBS138 may instruct the ISP to not respond to N probe requests from a useron 2.4 Ghz, but then respond to the N+1 probe request, thereby notpreventing the AP from using a channel in the 2.4 Ghz, but ratherencouraging it to seek an available channel in the 5 Ghz band to avoidthe delays in response to its probes.

Alternatively, MBS 138 may employ active steering. For example, MBS 138may blacklist (step 650) an AP in the 2.4 Ghz band.

It will be appreciated that process 600 may comprise feedbackfunctionality similar to that described with respect to steps 480 and/or550 in processes 400 and 500, respectively.

It will be appreciated by one of ordinary skill in the art that theembodiments described herein, either singly, or in combination, mayenable optimizing configuration of WiFi based residential/commercialnetworks by adapting known SON methods from the mobile space andaccounting for lack of physical control/access of/to the WiFi accesspoints. Implementation of some or all of these embodiments may thereforefacilitate a new emerging business model for ISPs to provide broadcoverage via community/residential WiFi networks.

It is appreciated that software components of the present invention may,if desired, be implemented in ROM (read only memory) form. The softwarecomponents may, generally, be implemented in hardware, if desired, usingconventional techniques. It is further appreciated that the softwarecomponents may be instantiated, for example: as a computer programproduct or on a tangible medium. In some cases, it may be possible toinstantiate the software components as a signal interpretable by anappropriate computer, although such an instantiation may be excluded incertain embodiments of the present invention.

It is appreciated that various features of the invention which are, forclarity, described in the contexts of separate embodiments may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention which are, for brevity, described in thecontext of a single embodiment may also be provided separately or in anysuitable subcombination.

It will be appreciated by persons skilled in the art that the presentinvention is not limited by what has been particularly shown anddescribed hereinabove. Rather the scope of the invention is defined bythe appended claims and equivalents thereof:

What is claimed is:
 1. A method for improving performance in aresidential/community WiFi network, the method implemented on aself-optimizing network (SON) server and comprising: receiving currentconfiguration details and local performance statistics from SON clientsinstalled in access points (APs) in said residential/community WiFinetwork, wherein at least one of said APs is a residential AP configuredto provide WiFi connectivity to both authorized users of saidresidential AP and a community of WiFi users not associated with saidresidential AP; analyzing at least said current configuration detailsand local performance statistics to identify performance issues in saidresidential/community WiFi network; determining remedial actions basedon said analyzing; and instructing said access points to perform saidremedial actions via said SON clients.
 2. The method according to claim1 and further comprising: receiving additional configuration andperformance details from an Internet service provider (ISP) associatedwith said APs, wherein said analyzing further comprises analyzing saidadditional configuration and performance details.
 3. The methodaccording to claim 2 and wherein: there are rogue APs in saidresidential/community WiFi network, wherein said rogue APs do not havesaid SON clients; and said analyzing further comprises inferring saidcurrent configuration details and performance for said rogue APs fromsaid additional configuration and performance details.
 4. The methodaccording to claim 1 and wherein one of said remedial actions isinstructing at least one of said APs to adjust power output.
 5. Themethod according to claim 4 and wherein said analyzing furthercomprises: analyzing a coverage level on a per SSID (service setidentifier) basis.
 6. The method according to claim 4 and wherein saidinstructing comprises: instructing said at least one of said APs toadjust power output to match a defined coverage threshold.
 7. The methodaccording to claim 1 and wherein one of said remedial actions isinstructing at least one of said APs to use a different channelfrequency.
 8. The method according to claim 7 and further comprising:clustering APs in said residential/community WiFi network; mapping aninter-cell dependency matrix (ICDM) for each of said APs in a givencluster; measuring performance for said given cluster; and identifyingat least one under-performing AP in said given cluster according to saidmeasuring, wherein said instructing comprises instructing said at leastone under-performing AP to switch to said different channel frequency.9. The method according to claim 8 and wherein said measuring comprisesat least one of: measuring interference per channel; measuring load perchannel; measuring AP capacity; measuring user received signal strengthindicator/signal-to-noise ratios (RSSI/SNRs); or measuring throughputsbetween said APs in said given cluster.
 10. The method according toclaim 8 and wherein said identifying comprises: minimizing a costfunction to determine which said at least one underperforming AP toswitch to which said different channel frequency.
 11. The methodaccording to claim 1 and wherein one of said remedial actions issteering at least one of said APs to use a specific frequency band. 12.The method according to claim 11 and wherein said analyzing comprises:determining which of said APs is a 5 Ghz band capable AP.
 13. The methodaccording to claim 11 and further comprising: adding said 5 Ghz bandcapable AP to a non-response list for 2.4 Ghz band probes in saidresidential/community WiFi network.
 14. The method according to claim 11and further comprising: adding said 5 Ghz band capable AP to adelayed-response list for 2.4 Ghz band probes in saidresidential/community WiFi network, wherein a first N probes receivedfrom a device in said delayed-response list are ignored.
 15. The methodaccording to claim 11 and further comprising: blacklisting said 5 Ghzband capable AP from 2.4 Ghz band operation.
 16. The method according toclaim 12 and wherein said instructing comprises: instructing said 5 Ghzband capable AP to use a 5 Ghz band channel.
 17. The method according toclaim 1 and further comprising: subsequent to said instructing,receiving feedback regarding AP performance; and repeating saidanalyzing in light of said feedback.
 18. The method according to claim17 and further comprising: limiting said repeating to prevent anoscillating effect wherein one or more of said remedial actions areperformed and reversed in a loop.
 19. A residential/community WiFinetwork self-optimizing network (SON) server comprising: means forreceiving current configuration details and local performance statisticsfrom SON clients installed in access points (APs) in saidresidential/community WiFi network, wherein at least one of said APs isa residential AP configured to provide WiFi connectivity to bothauthorized users of said residential AP and a community of WiFi usersnot associated with said residential AP; means for analyzing at leastsaid current configuration details and local performance statistics toidentify performance issues in said residential/community WiFi network;means for determining remedial actions based on said analyzing; andmeans for instructing said access points to perform said remedialactions via said SON clients.
 20. A residential/community WiFi networkself-optimizing network (SON) server comprising: an I/O module operativeto receive current configuration details and local performancestatistics from SON clients installed in access points (APs) in saidresidential/community WiFi network, wherein at least one of said APs isa residential AP configured to provide WiFi connectivity to bothauthorized users of said residential AP and a community of WiFi usersnot associated with said residential AP; a processor; and a centralizedSON application operative to be executed by said processor, wherein saidcentralized SON application comprises: a centralized dynamic channelassignment function operative to determine dynamic channel assignmentsfor said APs based on collected measurements received via said I/Omodule, a centralized power control (cPC) function operative to reduceor increase total transmit power for said APs based on a coveragethreshold, and a multi-band steering (MBS) function operative to steersaid APs to a frequency band based on their capabilities.